data-engineering

Audit a BigQuery SQL query or dbt model for potential cost traps before it hits production.

Review or design a data pipeline architecture based on requirements

Build a new analytics feature or dbt model guided by business requirements and data exploration

Audit changes to dbt Semantic Layer definitions for breaking changes and aggregation risks.

Analyzes BigQuery SQL and dbt configurations to predict and reduce query costs. Identifies full table scans, poor partitioning, and inefficient joins.

Analytics engineer for warehouse-backed dbt projects on BigQuery. Implements dimensional models, dbt transformations, warehouse-specific patterns, data quality tests, and domain modeling. Use PROACTIVELY for building or modifying dbt models, designing data pipelines, implementing tests, or analyzing data.

Dimensional modeling architect for dbt projects. Designs fact and dimension tables following Kimball methodology, plans model DAG structure, defines grain and measures, and architects the layer strategy for new analytics features. Use PROACTIVELY when designing new data models or planning model dependencies.

Analytics engineer specializing in dbt projects on a cloud data warehouse. Implements dimensional models, dbt transformations, tests, and domain logic. Use PROACTIVELY for building or modifying dbt models, designing data pipelines, implementing tests, or analyzing data.

Reviews changes to the dbt Semantic Layer, ensuring metrics and dimensions remain mathematically sound and backwards-compatible.

Use when reviewing BigQuery spend, query failure patterns, or scan inefficiencies -- identifying which jobs, users, or projects drive cost, or preparing optimization recommendations for a cost review.

Use when adding or reviewing data quality tests for dbt models in warehouse-backed analytics projects. Covers dbt generic tests, singular tests (assert_*.sql), accepted-values macros, dbt_utils patterns, cross-system consistency tests, and warehouse-oriented validation. Use when writing data tests, creating assert_*.sql files, testing business logic, or validating referential integrity.

Use when reviewing a new or changed dbt incremental model -- the strategy looks over-engineered, rebuild or delete behavior is unclear, or the model deviates from how the rest of the repo handles incrementals.

Use when a dbt change needs preflight before a PR, review, or merge -- changed models, snapshots, seeds, macros, or semantic YAML whose blast radius and test coverage are not yet known.

Use when creating or modifying dimensional dbt models in warehouse-backed analytics projects. Covers a four-layer warehouse architecture (sources/staging/core/marts), naming conventions, no-alias SQL rule, surrogate-key and missing-record patterns, incremental strategies, deduplication, and common project macros. Use when building fact tables, dimension tables, staging models, writing SQL, or designing tests.